Temporal modulation of signal/noise reveals processing units of a scale greater than letters in visual word recognition.
Bibliographic record
Abstract
Visual word recognition is largely based on the identification of the letters making up the stimulus (e.g. Pelli et al., 2003). Some studies, however, suggest that units of a greater scale than letters (ranging from transletter features to whole words) also contribute to performance. This claim rests on the negative impact of aLtErNaTiNg CaSe across consecutive letters on reading performance (e.g. Mayall et al., 1997; Pelli & Tillman, 2007). One drawback of case alternation is that it may require observers to switch perceptual tuning between the recognition of uppercase and lowercase letters for every consecutive letter. This means that the cost of case alternation may lie in the letter identification process itself, thus making the result inconclusive with respect to the size of the processing units involved in word recognition. Here, we assess whether units of a scale greater than the letter effectively contribute to visual word recognition. We used a random temporal modulation of the signal-to-noise ratio of the stimulus that was either applied simultaneously throughout all the letters in the word (homogeneous condition) or separately and independently for each letter (heterogeneous condition). Temporal sampling functions were made from the integration of sine waves of 5, 10, 15, and 20 Hz, each with a random amplitude and phase. Stimuli were displayed on a 120 Hz monitor, their exposure (i.e. target + noise) lasted 200 ms and the duration of visibility of each letter was equated across conditions. Our results show significantly better word recognition performance in the homogeneous condition (72.5 %) in comparison to the heterogeneous one (59.3 %), a finding that is verified in each individual participant. The advantage for a temporal modulation of signal/noise that applies simultaneously across all the letters in a word demonstrates a significant contribution of processing units of a scale larger than the single letter. Meeting abstract presented at VSS 2017
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".